Harnessing Artificial Intelligence for Cancer Prevention, Early Diagnosis, and Personalized Treatment

The field of oncology has witnessed remarkable strides in early detection and treatment, yet cancer remains a formidable threat with high mortality rates worldwide. Traditional methods, although effective to some extent, struggle to keep pace with the evolving nature of malignancies. Emerging technologies, particularly in Artificial Intelligence (AI), are poised to offer innovative solutions that surpass existing strategies. With machine learning (ML) and deep-learning (DL) methods, AI holds the potential to revolutionize the assessment of risk factors, enhance screening procedures, and develop personalized therapies. Recently, numerous AI-assisted models have been developed for the screening and identification of cancer-specific compounds; however, there is still a significant research gap in employing AI to evaluate the etiology and risk factors for certain malignancies, which could improve clinical outcomes dramatically.

This Research Topic aims to bridge this gap by exploring the transformative potential of AI in cancer care. By focusing on AI's role in early detection, risk stratification, and personalized interventions, the research seeks to advance our understanding and application of AI in oncology. This includes investigating the integration of supervised and unsupervised models that address ethical challenges and improve AI model efficacy in clinical settings. The overarching objective is to augment traditional methods with AI's capabilities to drive a deeper understanding of cancer dynamics and treatment modalities.

To gather further insights within the boundaries of AI applications in cancer prevention and treatment, we welcome articles addressing, but not limited to, the following themes:

- Development of AI models predicting individual cancer risk utilizing multi-omics datasets
AI-powered image analysis for early tumor detection
- Creation of personalized preventive measures by integrating genetic, environmental, and demographic factors
- Innovative advancements in cancer preventive measures
- Enhanced precision in diagnosis and treatment through AI techniques
- Artificial Intelligence's role in predicting disease outcomes with personalized therapy approaches

Specific sub-thematic areas include:

- Designing AI models with multi-omics datasets for cancer preventive measures
- Assessing etiological factors through AI for early-stage malignancies
- Clinical applications of AI models for personalized cancer therapy

Please note that manuscripts consisting solely of bioinformatics or computational analysis of public omics databases, without relevant functional validation (clinical cohort, biological validation in vitro, or in vivo), are out of scope for this Research Topic.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Cancer Prevention, Algorithms, Multi-Omics, Malignancies, Imaging-analysis

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.